How to Set Up a Hypothesis Test: Null versus Alternative

Null Hypothesis (1 of 4) - David Lane

Let me expand on the previous paragraph byquoting avery important passage from Edgington (1986). "Justas thereference set (read as "sampling distribution"for now) ofdata permutations is independent of the test statistics,so is thenull hypothesis. A difference between means may be used asa teststatistic, but the null hypothesis does not refer to adifferencebetween means. The null hypothesis, no matter what teststatistic isused, is that there is no differential effect of thetreatments forany of the subjects. ... Thus the alternative hypothesisis that themeasurement of at least one subject would have beendifferent underone of the other treatment conditions. Inferences aboutmeans must bebased on nonstatistical considerations; the randomizationtest doesnot justify them." (p. 531)

The null hypothesis is an hypothesis about a population parameter

Not so long ago, people believed that the world was flat.

Null hypothesis, H0: The world is flat.Alternate hypothesis: The world is round.Several scientists, including , set out to disprove the null hypothesis. This eventually led to the rejection of the null and the acceptance of the alternate. Most people accepted it — the ones that didn’t created the !. What would have happened if Copernicus had not disproved the it and merely proved the alternate? No one would have listened to him. In order to change people’s thinking, he first had to prove that their thinking was wrong.

Before actually conducting a hypothesis test, you have to put two possible hypotheses on the table — the null hypothesis is one of them. But, if the null hypothesis is rejected (that is, there was sufficient evidence against it), what’s your alternative going to be? Actually, three possibilities exist for the second (or alternative) hypothesis, denoted Ha. Here they are, along with their shorthand notations in the context of the pie example:

Support or Reject Null Hypothesis in Easy Steps

Typically in a hypothesis test, the claim being made is about a population (one number that characterizes the entire population). Because parameters tend to be unknown quantities, everyone wants to make claims about what their values may be. For example, the claim that 25% (or 0.25) of all women have varicose veins is a claim about the proportion (that’s the ) of all women (that’s the ) who have varicose veins (that’s the — having or not having varicose veins).

Explainer: what is a null hypothesis? - The Conversation

When you set up a hypothesis test to determine the validity of a statistical claim, you need to define both a null hypothesis and an alternative hypothesis.

5 Differences between Null and Alternative Hypothesis …

You need descriptive statistics for three reasons. First, if you don’t have enough variance on the variables of interest, you can’t test your null hypothesis. If everyone is white or no one is obese, you don’t have the right dataset for your study. Second, you are going to need to include a table of sample statistics in your paper. This should include standard demographic variables – age, sex, education, income and race are the main ones. Last, and not necessarily least, descriptive statistics will give you some insight into how your data are coded and distributed.

How to Determine a p-Value When Testing a Null Hypothesis

In the second experiment, you are going to put human volunteers with high blood pressure on a strict low-salt diet and see how much their blood pressure goes down. Everyone will be confined to a hospital for a month and fed either a normal diet, or the same foods with half as much salt. For this experiment, you wouldn't be very interested in the P value, as based on prior research in animals and humans, you are already quite certain that reducing salt intake will lower blood pressure; you're pretty sure that the null hypothesis that "Salt intake has no effect on blood pressure" is false. Instead, you are very interested to know how much the blood pressure goes down. Reducing salt intake in half is a big deal, and if it only reduces blood pressure by 1 mm Hg, the tiny gain in life expectancy wouldn't be worth a lifetime of bland food and obsessive label-reading. If it reduces blood pressure by 20 mm with a confidence interval of ±5 mm, it might be worth it. So you should estimate the effect size (the difference in blood pressure between the diets) and the confidence interval on the difference.

After you do a statistical test, you are either going to reject or accept the null hypothesis. Rejecting the null hypothesis means that you conclude that the null hypothesis is not true; in our chicken sex example, you would conclude that the true proportion of male chicks, if you gave chocolate to an infinite number of chicken mothers, would be less than 50%.

When you reject a null hypothesis, there's a chance that you're making a mistake. The null hypothesis might really be true, and it may be that your experimental results deviate from the null hypothesis purely as a result of chance. In a sample of 48 chickens, it's possible to get 17 male chickens purely by chance; it's even possible (although extremely unlikely) to get 0 male and 48 female chickens purely by chance, even though the true proportion is 50% males. This is why we never say we "prove" something in science; there's always a chance, however miniscule, that our data are fooling us and deviate from the null hypothesis purely due to chance. When your data fool you into rejecting the null hypothesis even though it's true, it's called a "false positive," or a "Type I error." So another way of defining the P value is the probability of getting a false positive like the one you've observed, if the null hypothesis is true.

Another way your data can fool you is when you don't reject the null hypothesis, even though it's not true. If the true proportion of female chicks is 51%, the null hypothesis of a 50% proportion is not true, but you're unlikely to get a significant difference from the null hypothesis unless you have a huge sample size. Failing to reject the null hypothesis, even though it's not true, is a "false negative" or "Type II error." This is why we never say that our data shows the null hypothesis to be true; all we can say is that we haven't rejected the null hypothesis.

Null and Alternative Hypothesis | Real Statistics Using Excel

In Chapters 4 and 5, the use of randomizationteststatistics involving differences between means did notimply a testof a null hypothesis of no difference in mean treatmenteffects. Thenull hypothesis was no difference in the treatment effectfor anysubject, and that is the same null hypothesis tested if weexpect adifference in variability of treatment effects and use ateststatistic sensitive to that property. Whether we employ ateststatistic sensitive to mean differences, differences invariability,or differences in skewness of treatment effects depends onourexpectation of the nature of the effect that may exist,but thechoice does not alter the null hypothesis that is tested,which isthat of no treatment effect ... ."

Journal of Articles in Support of the Null Hypothesis

Every hypothesis test contains a set of two opposing statements, or hypotheses, about a population parameter. The first hypothesis is called the denoted H0. The null hypothesis always states that the population parameter is to the claimed value. For example, if the claim is that the average time to make a name-brand ready-mix pie is five minutes, the statistical shorthand notation for the null hypothesis in this case would be as follows: